Voice tone quality similarity measurement method for musical instrument under tone quality migration

A similarity measurement and timbre technology, applied in instruments, speech analysis, speech recognition, etc., can solve the problem of no human voice chromatic similarity description

Inactive Publication Date: 2018-08-17
FUZHOU UNIV
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AI Technical Summary

Problems solved by technology

[0015] Although the existing research on human voice timbre can effectively solve some sin...

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  • Voice tone quality similarity measurement method for musical instrument under tone quality migration
  • Voice tone quality similarity measurement method for musical instrument under tone quality migration
  • Voice tone quality similarity measurement method for musical instrument under tone quality migration

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Embodiment Construction

[0030] The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments.

[0031] The present invention provides a method for measuring the similarity of human voice under the transfer of musical instrument timbre, which includes the following steps: Step S1: Obtaining musical instrument audio files, constructing and training a deep convolutional neural network model, and obtaining a trained IDCNN model; Step S2 : Obtain the vocal a cappella audio file, use the human voice audio data to fine-tune the trained IDCNN model, obtain the representation model HDCNN of the human voice, and the HDCNN model outputs the timbre vector of the human voice; Step S3: Calculate the cosine degree between different timbre vectors, Get the similarity between different vocal timbres. For a schematic diagram of the main process, see figure 1 .

[0032] In an embodiment of the present invention, the step S1 specifically includes the follo...

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Abstract

The invention relates to a voice tone quality similarity measurement method for a musical instrument under tone quality migration. The method comprises the steps: firstly calculating logarithmic Mel-spectrum characteristics of sufficient and balanced musical instrument data set audio, obtaining a characteristic matrix, training a deep convolution neural network model through the musical instrumentdata, and obtaining a trained deep convolution neural network model; secondly calculating and obtaining the logarithmic Mel-spectrum characteristics of insufficient and imbalanced voice audio data set through the same method; thirdly carrying out the fine tuning of the trained deep convolution neural network model through a fine tuning method based on the transfer learning, and obtaining a voicetone representation model, wherein the output of the model is a voice tone vector; finally achieving the measurement of the similarity of voice tones through calculating the cosine similarity of tonevectors.

Description

technical field [0001] The invention belongs to the field of audio signal processing in the field of singing, in particular to a method for measuring the similarity of human voice under the transition of musical instrument timbre. Background technique [0002] The American National Institute of Standardization defines timbre as follows, "timbre refers to a certain attribute of sound produced by hearing, and the listener can judge the difference between two sounds presented in the same way with the same pitch and loudness. ". Therefore, the vocal timbre during singing refers to the voice characteristics that people use to identify the specific singer when different singers sing the same song. [0003] Spectrograms are commonly used in acoustic experiments for sound analysis. The spectrogram can display the characteristics of amplitude changing with frequency and time. The ordinate represents frequency, and the abscissa represents time. The magnitude of the amplitude is repr...

Claims

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Application Information

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IPC IPC(8): G10L25/81G10L15/06G10L15/16G10L21/0224G10L21/0232G10L21/0272G10L25/18G10L25/24G10L25/30
CPCG10L15/063G10L15/16G10L21/0224G10L21/0232G10L21/0272G10L25/18G10L25/24G10L25/30G10L25/81
Inventor 余春艳齐子铭刘煌张栋
Owner FUZHOU UNIV
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